Methods, Protocol and System for Customizing Self-driving Motor Vehicles

ABSTRACT

A protocol and a system for customizing driving operation of and legalizing self-driving motor vehicles are introduced as an alternative solution to state-of-the-art technologies in development and marketing of the self-driving motor vehicles, promising to enable a self-driving motor vehicle to provide an experience as if being driven by the mind of a passenger the first time it serves on a public roadway.

TECHNICAL FIELD

Artificial intelligence; self-driving motor vehicles.

BACKGROUND

Driving automation based on artificial intelligence has evolved now to astage of road tests by self-driving motor vehicle manufacturers. Amongother issues, accidents are occasionally reported calling for moreimprovements. A self-driving motor vehicle could be viewed as if a robotsits on a conventional motor vehicle, though it does not take the shapeof what is commonly presented or perceived comprising a Sensing System,a Control System and an Activation System, while the conventional motorvehicle should be altered significantly for a better integration, asillustrated in FIG. 1. A self-driving motor vehicle drives itself fromone start point to a destination set by a user or a remote controllerthrough a wireless communication system or an electronic media deviceand guided by an automatic navigational system with or without involvinga user in the vehicle. It can carry one or more passengers or nopassengers, for example when it is sent for a passenger. A robot on theself-driving motor vehicle monitors the scenarios in driving includingdetecting roadway conditions and traffic signs and/or signals andvarious other factors that impact its operation, matching againstscenarios modelled in its internal data structures and determines properoperation behaviors according to the traffic rules, just like humandrivers do. However, driving as a human activity has more attributesthan just moving or transportation, comprising safety, comfort,exercise, sport and so on, which vary according to experiences, favors,moral and/or ethics traits of individual drivers or passengers amongother things. In a scenario of an emergency or an accident, differentpassengers or riders tend to have different preferred operationbehaviors by a self-driving motor vehicle, concerning responsibilities,liabilities, and damage controls to different parties involved, andpossible other issues of conflicting interest, which could be verydifficult if ever possible for a self-driving motor vehicle with genericfactory settings to render operation behaviors preferred by eachindividual passenger or rider in such a scenario. From vehicle operationpoint of view, a fundamental difference between a conventional and aself-driving motor vehicle is that the former provides an essentialplatform for a driver to exercise the operation, while the latter triesto provide a ubiquitous platform essentially without involving a driverin its operation. Although there have been vigorous researches onself-driving motor vehicles adapting to a passenger or rider after it ison the road in the state of art technologies, rare work is reported oncustomizing a self-driving motor vehicle in manufacture of aself-driving motor vehicle or before a self-driving motor vehicle ispractically used by a user. There is no country or area in the worldwhere a vehicle licensee has been issued to a self-driving motor vehicletoday.

SUMMARY OF THE INVENTION

A key idea for this invention is to have a self-driving motor vehiclemanufactured and/or trained as if being driven by the mind instead ofhands of each passenger it serves the first day on a journey.

Disclosed is a method of customizing a self-driving motor vehicle bypersonalizing and/or disciplining the self-driving motor vehicle beforethe self-driving motor vehicle is practically used in a service on apublic roadway and applying and/or refining the customizing duringdriving.

Introduced is a customized driving protocol for self-driving motorvehicles comprising:

obtaining a training collection of data of a plurality of scenarios anda collection of data of operation behaviors of a self-driving motorvehicle in each of the scenarios, wherein the training collection areverified and/or verifiable by simulation and/or road tests and theoperation behaviors of a self-driving motor vehicle in each of thescenarios are lawful;

acquiring a scenario-user-choice pair data set based on the trainingcollection and acquiring a user profile data set of one or more users ofthe self-driving motor vehicle in manufacturing the self-driving motorvehicle and/or prior to driving the self-driving motor vehicle on apublic roadway in a practical service;

identifying a current rider, or one of current riders of theself-driving motor vehicle to be the current user, wherein data havebeen acquired in the entry of the current user in thescenario-user-choice pair data set and in the entry of the current userin the user profile data set of the self-driving motor vehicle prior tothe self-driving motor vehicle being practically used on a publicroadway;

driving the self-driving motor vehicle based on the scenario-user-choicepair data set and/or the user profile data set, comprising:

finding a match between a current scenario and a scenario in ascenario-user-choice pair in the entry of the current user in thescenario-user-choice pair data set;

operating the self-driving motor vehicle according to the user choice inthe scenario-user-choice pair if the match is found, and the currentuser assumes at least partial responsibilities for consequences of theoperating, or

generating operation behaviors of the self-driving motor vehicle if thematch is not found and estimating probability for the current user tochoose each of the operation behaviors referencing data in the entry ofthe current user in the user profile data set and data from statisticalanalysis of data of motor vehicle driving records and/or psychologicalbehavior of drivers, and electing an operation behavior having thelargest probability to operate the self-driving motor vehicle.

Disclosed is also a system for customizing and legalizing a self-drivingmotor vehicle based on the customized driving protocol.

An embodiment of designing a training collection and embodiments ofmatching a scenario are also presented.

BRIEF DISCUSSION OF DRAWINGS

FIG. 1 Illustration of a functional structure of a self-driving motorvehicle.

FIG. 2 Illustration of categorized response time interval to roadway andtraffic events, the shaded area around T1 and T2 indicate it should beconsidered as a zone with a boundary varying from model to model, andfrom time to time.

FIG. 3 Illustration of the procedures to customize a self-driving motorvehicle.

FIG. 4 Table 1, Example impacts on operation of a self-driving motorvehicle by the user data sets.

FIG. 5 Illustration of how to apply user data in self-driving.

FIG. 6 Illustration of the process for legalizing a self-driving motorvehicle.

FIG. 7 Illustration of a comparison of different operation behaviors ofcustomized and non-customized generic self-driving motor vehicles.

FIG. 8 Illustration an expansion of FIG. 7. module 880 is a double arrowhead indicating a probabilistic correlation between traits of differentethics groups of users to different preferred operation behaviors in ascenario, while module 881 indicating a probability density distributionof user ethics groups. module 818 indicate the probabilisticrelationship between user traits to user preferred operation behaviorsin design of a training collection and in applying the user profile dataassisting vehicle operation.

DETAILED DESCRIPTION OF THE INVENTION

The following descriptions and illustrated example embodiments areintended to explain the invention without limiting the scope of thisinvention.

Scenario hereafter in this disclosure is defined as a data set of asnapshot or a sequence of snapshots of factors in a driving situationthat impact a driving operation of a self-driving motor vehicle,comprising for example: natural environment comprising seasons, weather,and temperature and humidity; social environment comprising socialorder, governing laws including traffic laws and rules, and communityethics in driving; roadway conditions comprising number of lanes, localdriveways or divided multiple lane freeways, ground types, trafficcontrol signals, road signs and obstacles; roadway sharing objectscomprising pedestrians, non-motor vehicles, other motor vehicles,sideway scenes, sideway buildings or other objects and/or views thatcould block the sight of a driver or the Sensing System of aself-driving motor vehicle; traffic conditions comprising the visibilityof conventional driver and of a self-driving motor vehicle, degree ofcongestion, the traffic speed and number of passing motorvehicles/number of lanes in a road section; operation conditions of aself-driving motor vehicle comprising speed, response time to drivingevents, mechanical and electrical performances and number of passengerson board. The data set of a scenario comprises a descriptive data partand optionally an implementation-dependent numeric data part, which aremeasured, categorized, encoded, quantized and structured numeric data ofdata in the descriptive data part.

A user denotes a passenger and/or a rider and/or an owner of aself-driving motor vehicle who is also a passenger or a rider.

A training collection denotes a training collection of data of aplurality of scenarios and a collection of data of operation behaviorsof a self-driving motor vehicle in each of the scenarios, wherein theoperation behaviors comprising one basic vehicle operation or asynchronized sequence of basic vehicle operations comprising speedingup, speeding down, moving forward or backing up, making turns, braking,lights and sound controls, and the data comprising a descriptive datapart and optionally an implementation-dependent numeric data part, whichcomprises measured, categorized, encoded, quantized and structurednumeric data of data in the descriptive data part. Further, as anexample of design, a training collection may comprise a CoordinateMatching Range Vector Set associating with each scenario and each ofoperation behaviors in the scenario for facilitating scenario matchingduring a driving.

A self-driving motor vehicle keeps monitoring scenarios during a drivingincluding roadway traffic and the vehicle conditions by its SensingSystem, and any event prompting for a responding adjustment of itsoperation could be analyzed to fall into one of the three conceptuallycategorized response time intervals, taking into account the distance ofan involved object to and the speed of the vehicle, the time needed forthe robot to run algorithms and Activation System, and for theactivation to take effect, as illustrated in FIG. 2. The parametersseparating the zones are a range of values overlapping between theadjacent zones, which are vehicle model dependent and scenariodependent. The interval between time 0 to T1 is hereby referred to asThe Blinking Zone, wherein the robot can virtually do little or nothingto address an event or avoid an accident except to minimize the damagesand send out alarms if there is an accident. The interval between T1 toT2 is referred to as The Emergency Zone, wherein actions could be takento address an event or avoid an accident or let an accident happen inone way or another that would put different risks of damages to theuser, the vehicle of the user and/or other parties who are involved inthe accident comprising other vehicles or pedestrians who happens toshare the roadway at the time. The interval from T2 beyond is referredto as the Cruise Zone, wherein the roadway and traffic events are easilymanageable, and chance of an accident is very small. Corresponding toeach interval, there are sets of data acquired reflecting attributes ofa user comprising preferred behaviors in various scenarios, preferreddriving styles, and/or moral or ethics traits, which will be used by therobot in control of the vehicle operations.

From customizing vehicle operation point of view, scenarios could bealternatively categorized into a first set named hereby as the Class IScenarios, wherein driving could be managed by a Control Systemessentially based on a generic factory design, and a second set namedhereby as the Class II Scenarios wherein a self-driving motor vehicleoperates driving comprising requiring according to a customized drivingprotocol. The scenarios in the second set comprise for example presenceof a conflict of interests involving attributes of a user comprisingmoral and/or ethics traits, and/or traffic rules and laws, and/or risksto safety of a user and/or a self-driving motor vehicle and/or otherparties sharing roadways, and/or presence of an uncertainty of operatinga self-driving motor vehicle to match the intention of a user inresponse to abrupt events during a driving. To optimize driving bycustomizing the operation behavior of a self-driving motor vehicle, theself-driving motor vehicle is trained prior to its being granted alicense to carry a passenger in a practical service on a public roadwayby two distinctive yet correlated user data sets comprising a userprofile data set and a scenario-user-choice pair data set. The term ofscenario-user-choice pair is an abbreviation for “scenario and userchoice pair” indicating a paired combination between a scenario and apreferred user choice of an operation behavior of a self-driving motorvehicle in the scenario, as illustrated in FIG. 7. Ascenario-user-choice pair data set comprise entries of all users,wherein each entry matches an individual user comprising all thescenario-user-choice pairs of the user in a form of a data structure.Subsequently, each scenario-user-choice pair comprises a descriptivedata part of a scenario and a user choice of an operation behavior inthe scenario and optionally an implementation-dependent numeric datapart comprising measured, categorized, encoded, quantized and structurednumeric data of data in the descriptive data part. A user profile dataset comprises entries of all users, wherein each entry matches anindividual user comprising a user background section and a user traitssection, and each section is comprised a descriptive data part andoptionally an implementation-dependent numeric data part comprisingmeasured, categorized, encoded, quantized and structured numeric data ofdata in the descriptive data part in both sections. The organization ofthe data structures in the data sets is intended to make data in thedescriptive data part accessible by any implementers, and as aninterface to users during training self-driving motor vehicles, whiledata in the numeric data part is for facilitating real-time processingby a Control System, and therefore is implementation-dependent. Theconcept behind a scenario-user-choice pair comes from the observationthat there could be multiple options to operate the vehicle in ascenario, and a Control System might have difficulty to figure out anoptimal solution matching the intention of a current user, without priorknowledge of the attributes and/or preferences of the current user. Thebackground section of a user profile data comprises data of a userincluding age, gender, body height, body weight, profession, marriagestatus, living area, education level, searchable public recordscomprising of driving, medical, disability, insurance, credit, andcrimes; while the traits section comprises data of a user of drivingand/or riding styles, and/or the moral and/or ethics traits of a user.

To acquire a scenario-user-choice pair data set, a training collectionis obtained by designing a training collection and/or receiving adesigned training collection or a combination of designing andreceiving. A training collection are verified and/or verifiable bysimulation and/or road tests and each of the collection of operationbehaviors of a self-driving motor vehicle in each of the scenarios in atraining collection are lawful. Design of a training collection andverification of a training collection by simulation and/or analysis ofthe statistical driving data is feasible within the state of the art,while verification by road tests is also feasible in at least part ofthe scenarios in a training collection, though less efficient. Atraining collection could be designed by a manufacturer of self-drivingmotor vehicles and/or an institution other than a manufacturer ofself-driving motor vehicles and/or an individual designer for a specificvehicle model or as a design for a range of vehicle models, inaccordance with the laws and rules of areas or countries wherever theself-driving motor vehicles are to be used by applying sophisticatedalgorithms of artificial intelligence or by trained professionalsoperating some relatively simple statistical tools processing data fromconventional vehicle driving records, simulation and/or road tests ofself-driving motor vehicles, community driving behaviors and/or moraland/or ethics traits of the areas and/or the countries.

Below is a description of an embodiment of such a design, which servesto illustrate the invention without limiting the scope of the invention.

C1. picking up scenarios into a training collection:

collecting data of a collection of scenarios into a candidate set of atraining collection based on a statistical analysis of data of motorvehicle driving records, and/or data from simulation of motor vehicledriving;

assigning a first weighting factor Wc11 to each of the scenarios in theset, wherein the value of Wc11 comprising proportional to appearanceprobability of the scenario based on a statistical analysis of the dataof the scenarios in the candidate set;

assigning a second weighting factor Wc12 to each of the scenarios,wherein the value of Wc12 comprising proportional to level of risk ofsafety of and/or damage to properties of parties involved in thescenario in relation to the intention of different drivers operating amotor vehicle in the scenario based on a statistical analysis of thedata of the scenarios in the candidate set;

assigning a third weighting factor Wc13 to a scenario, wherein the valueof Wc13 comprising proportional to level of uncertain of operating amotor vehicle in relation to the intention of different drivers inresponse to abrupt driving events in the scenario based on a statisticalanalysis of the data of the scenarios in the candidate set;

finding a combined weight Wc10 comprising by a weighting average of thethree weighting factors;

sorting the candidate set in a descending order of the combined weightWc10;

selecting data of scenarios in the candidate set into the trainingcollection referencing in a top-down order to the combined weight Wc10,until the combined weight Wc10 being smaller than a first adjustablethreshold Wc1t;

C2. finding a candidate data set of a collection of lawful operationbehaviors in each scenario in the training collection:

finding a candidate data set of operation behaviors for each scenario inthe training collection from a collection of data of operation behaviorsin the scenario based on a statistical analysis of data of motor vehicledriving records, and/or data from simulation of motor vehicle driving;

finding the probability of appearance of each of the operation behaviorsin the candidate set by a statistical analysis;

removing from the candidate set of data of operation behaviors unlawfuloperation behaviors;

removing from the candidate set of data of operation behaviors withprobability of appearance smaller than a second adjustable thresholdPc2;

C3. establishing user groups with similar psychological behavior patternof moral and/or ethics traits:

establishing a psychological behavior probability density distributionbased on a statistical analysis of data of motor vehicle drivingrecords, and/or data from simulation of motor vehicle driving relatingthe moral and/or ethics traits of a congregation of drivers and/orpassengers, comprising:

forming a one-dimensional probability density distribution betweenextreme selfish at one side and altruism at the other side, or amultiple dimensional user psychological behavior probability densitydistribution, and

dividing by an adjustable segment probability value the entiredistribution domain into a plurality of segments wherein each segmentcorresponding to a congregation of a group of users with similarpsychological behavior pattern of moral and/or ethics traits;

removing user groups with a segment probability smaller than a thirdadjustable threshold Pc3;

C4. electing an operation behavior from the candidate set of operationbehaviors associating with data of a scenario in a training collection:

determining based on a statistical analysis of data of motor vehicledriving records, and/or data from simulation of motor vehicle drivingand data of driving style and/or moral and/or ethics traits of driversand/or passengers a probability P32[i][j] for a resulting user group iin step C3 to select an operation behavior j in the resulting candidateset of data of operation behaviors in step C2,

wherein i=(1,2 . . . , L), j=(1, 2 . . . , M); L denotes the number ofthe total resulting user groups in step of C3 and M denotes the numberof total operation behaviors in a resulting candidate set in step of C2;

electing an operation behavior from the resulting candidate set in stepof C2 into the training collection as an operation behavior for a userto choose to form a scenario-user-choice pair if the probabilityP32[i][j] being larger than an adjustable threshold P32t, or a usergroup dependent adjustable threshold P32t[i];

removing from the resulting candidate set the operation behavior electedabove to avoid duplicating;

C5. optimizing the design for achieving a balancing between coverage ofthe scopes of the data and efficiency in actual usage of a trainingcollection by adjusting the involved thresholds and factors;

C6. calculating a Coordinate Matching Range Vector Set associating withdata of each scenario and data of each operation behavior in thescenario in the training collection for scenario matching in a driving,wherein:

an algorithm for scenario matching comprising:

representing by a vector Cij in real space the numeric data part of ascenario in a training collection measured, categorized, encoded,quantized and structured using a protocol of data in the descriptivedata part of the scenario,

Cij=(Cij[1], . . . Cij[n]),  [1]

wherein Cij[k] is the coordinate of the K^(th) component of vector Cijand (k=1, 2 . . . n),0<Cij[k]≤1;

representing by a vector of n dimension Ci in real space the numericdata part of a current scenario measured, categorized, encoded,quantized and structured using the same protocol of data in thedescriptive data part of the scenario,

Ci=(Ci[1], . . . Ci[n]),  [2]

wherein Ci[k] is coordinate of the K^(th) component of vector Ci and(k=1, 2 . . . n), 0<Ci[k]≤1; and hereafter in this exampleimplementation, any coordinate segment between two coordinate boundaryvalues of a component of a vector form a coordinate range of thecomponent of the vector, wherein each coordinate represent acorresponding monotonic measurement of a factor of the data set of ascenario, such that if any two boundary values of a coordinate segmentsatisfy a matching condition, all the coordinates within the segmentsatisfy the matching condition;

letting Sij representing a similarity measurement between the twovectors Ci and Cij, and

Sij=(Σ(Ci[k]-Cij[k])∧2×α_(k)/(n×Σα_(k)))∧0.5, (k=1,2, . . . , n),  [3]

wherein α_(k) is a weighting factor for coordinate of the K^(th)component, 0<Cij[k]≤1:0<Ci[k]≤1: 0<α_(k)≤1:

if Sij is smaller than an adjustable threshold Tij:

determining an adjustable Matching Boundary Vector Cijb around Cij basedon a statistical analysis of data of motor vehicle records and/or datafrom simulations and road tests of motor vehicles, and

Cijb=[(Cijbmin[1],Cijbmax[1]), . . . (Cijbmin[n],Cijbmax[n])],  [4]

wherein 0<Cijbmin[k]≤1, 0<Cijbmax[k]≤1, and

Cijbmin[k] is the minimum coordinate of the k^(th) component of Cijb,and Cijbmax[k] is the maximum coordinate of the k^(th) component ofCijb, (k=1, 2, . . . , n);

Cijbmin[k]≤Cij [k]≤Cijbmax[k], (k=1, 2, . . . , n);

and Ci[k] is also within a range of a Matching Boundary Vector Cijb,

Cijbmin[k]≤Ci[k]≤Cijbmax[k], (k=1, 2, . . . , n);

letting Pij representing an operation behavior in scenario Cij, if Pijis as effective in scenario Ci or the probability for Pij to be aseffective in scenario Ci is larger than an adjustable threshold Wij,asserting scenario Ci to be a match for scenario Cij under the conditionof Pij;

narrowing down and/or refining the search range comprising:

using a range granularity adjusting factor m equally dividing the rangeof each coordinate of each component of Coordinates Matching RangeVector Cijb into m congruent segments with a range threshold Tijbs[k]calculated by:

Tijbs[k]=(Cijbmax[k]-Cijbmin[k])/m; (k=1, 2, . . . n);  [5]

expanding the Coordinates Matching Range Vector Cijb into an array Cijbacomprising coordinate segments with smaller range values in eachcomponent, and

$\begin{matrix}{{Cijba} = \begin{matrix}{( {{{{Cijbmin}\lbrack 1\rbrack}\lbrack 1\rbrack},{{{Cijbmax}\lbrack 1\rbrack}\lbrack 1\rbrack}} ),} & {( {{{{Cijbmin}\lbrack 1\rbrack}\lbrack 2\rbrack},{{{Cijbmax}\lbrack 1\rbrack}\lbrack 2\rbrack}} ),} & {\ldots,} & ( {{{{Cijbmin}\lbrack 1\rbrack}\lbrack m\rbrack},{{{Cijbmax}\lbrack 1\rbrack}\lbrack m\rbrack}} ) \\\vdots & \vdots & {\ldots,} & \vdots \\( {{{{Cijbmin}\lbrack n\rbrack}\lbrack 1\rbrack},{{{Cijbmax}\lbrack n\rbrack}\lbrack 1\rbrack}} ) & ( {{{{Cijbmin}\lbrack n\rbrack}\lbrack 2\rbrack},{{{Cijbmax}\lbrack n\rbrack}\lbrack n\rbrack}} ) & {\ldots,} & ( {{{{Cijbmin}\lbrack n\rbrack}\lbrack m\rbrack},{{{Cijbmax}\lbrack n\rbrack}\lbrack m\rbrack}} )\end{matrix}} & \lbrack 6\rbrack\end{matrix}$

wherein,

Cijbmin[k][1] is the minimum value of the L^(th) segment of the K^(th)component of the Coordinates Matching Range Vector Cijb, and

Cijbmax[k][1] is the maximum value of the L^(th) segment of the K^(th)component of the Coordinates Matching Range Vector Cijb, and

Cijbmax[k][1]=Cijbmin [k][1+1], (k=1, 2, . . . n) (1=1, 2, . . . , m-1),

Cijbmax [k][1] is equal to Cijbmin[k]; and Cijbmax [k][m] is equal toCijbmax[k], respectively;

constructing a candidate matching vector of a scenario C_(ji) comprisedof n coordinates with each of the coordinates from a coordinate of aboundary point in a row of Cijba;

finding all the vector C_(ji) matching Cij based on the algorithm forscenario matching through a maximum of n∧m tests and putting thematching vectors into a candidate set;

constructing a Coordinate Matching Range Vector Cijr, wherein eachcoordinate is composed of a pair of coordinates comprising a firstcoordinate from a coordinate of a component of a vector in the candidateset and a second coordinate from a coordinate of the same component ofvector Cij, wherein the range of the pair of coordinates comprising amatching range of the coordinate values of the component;

fine-tuning the range granularity adjusting factor m to achieve acompromise between the granularity of search range, the computationcomplexity online and offline, and the data size of Coordinate MatchingRange Vectors;

C7. organizing all Coordinate Matching Range Vectors into a structuredCoordinate Matching Range Vector Set Cijra associating with data of eachof the scenarios and data of each of the operation behaviors in the eachof the scenarios in the training collection.

For a scenario-user-choice pair in a scenario-user-choice pair data set,the Coordinate Matching Range Vector Set that comes along with ascenario-user-choice pair demands a considerable real-time storagespace. However, generating the Coordinate Matching Range Vector Setcould be conducted off the line and/or during design of a trainingcollection, which does not consume real-time computing resources. Thesedata sets could be further compressed and structured prior to and/orafter being acquired in a scenario-user-choice pair data set and storedin a real-time storage media. A combination of a real-time matchingalgorithm with a searching-based matching could provide a balancedspace-time trade-off.

It should be noted that the above embodiment of designing a trainingcollection is only an illustrative implementation, and different methodscomprising multilayered relational data base could be deployed to getscenarios measured, categorized, encoded, quantized and structured, andscenarios matching could find various implementations in state of artself-driving motor vehicles designs by techniques including machinelearning and artificial intelligence.

Customizing a self-driving motor vehicle starts by acquiring ascenario-user-choice pair data set based on a training collection in aninitialization process, which could take place in manufacture of aself-driving motor vehicle and/or before the vehicle is practically usedon a public roadway. The initialization process is carried out by atesting apparatus comprising a stand-alone testing apparatus and/or ahuman tester operating a testing apparatus and/or a robot of aself-driving motor vehicle, using a multi-media human-machine interfaceconducting the steps of:

identifying a user;

informing the user of a user choice of an operation behavior in ascenario comprising a binding commitment between a self-driving motorvehicle and the user, wherein the self-driving motor vehicle operatesaccording to the operation behavior of the user choice in a matchedscenario, the user assumes at least partial responsibility for theconsequence of the operation behavior;

obtaining a consent from the user to the binding commitment;

presenting to the user data of one scenario at a time of the scenariosin a training collection and data of each of operation behaviors of aself-driving vehicle in the scenario in the training collection;

informing the user of the at least partial responsibility for theconsequence of the each of the operation behaviors in the scenario;

obtaining a choice from the user of an operation behavior in thescenario to form a scenario-user-choice pair of the scenario and theoperation behavior;

storing data of the scenario-user-choice pair in an entry of the user ina scenario-user-choice pair data set;

repeating the steps from presenting to storing for every scenario in thetraining collection; and/or receiving obtained data ofscenario-user-choice pairs into an entry of the user in ascenario-user-choice pair data set of a self-driving motor vehicle andconfirming and/or updating the scenario-user-choice pair data set withthe user prior to the self-driving motor vehicle being practically usedby the user in a service on a public roadway.

A description of the partial responsibility and/or consequence of theoperating for a scenario-user-choice pair is illustrated in The Example1 below.

The interactive interface between a testing apparatus and the user couldbe of a visual media comprising a touch screen panel for display andinput, or an audio media comprising a speaker announcement combined witha microphone and a speech recognition module to take the inputs, or acombination thereof, for users without vision or hearing disabilities.For user with disabilities, however, an assistant to the user could helpwith the initialization to use the above interactive interfaces, or anadaptive device could be designed and installed.

In addition to a scenario-user-choice pair data set, a user profile dataset is also acquired in an initialization process. Data in thebackground section of a user profile data set are acquired before a userpurchases or is granted a permit to use a service of a self-drivingmotor vehicle and/or before a user driving a self-driving motor vehicleson a public roadway in a practical service, by a testing apparatusthrough a human-machine interactive interface to obtain informationprovided by a user and/or research by a testing apparatus through awireless communication system or an electronic media device whereinrelevant user data are stored. The obtained background data of the userare stored in the background section of the entry of the user in a userprofile data set.

A testing apparatus extracts trait of the user by analyzing the acquireddata of the user in the scenario-user-choice pair data set andbackground data in the background section of the user profile data setbased on behavior modeling, factory tests and statistical drivingrecords and get the extracted user traits data stored in the traitssection of the entry of the user in a user profile data set. Thescenario-user-choice pair data set and/or the user profile data set datasets could be partially or fully acquired in manufacture of aself-driving motor vehicle and/or prior to a user purchasing aself-driving motor vehicle and delivered to the robot of a self-drivingmotor vehicle and be confirmed and updated if necessary by a testingapparatus and a current user before a self-driving motor vehicle ispractically used on a public roadway.

An example of impact on operation by scenario-user-choice pair data isillustrated in FIG. 4 Table 1. Since how to handle events in theEmergency Zone between T1 and T2 is most critical and controversial tothe safety behavior of a self-driving motor vehicle, some examples aredesigned and given below as an illustration.

EXAMPLE 1

A self-driving motor vehicle is driving on a roadway at a normal speedapproaching an intersection with a green light, a bicycle suddenly runsred light from one side of the roadway appearing in front of theself-driving motor vehicle. The robot finds braking the vehicle is toolate to avoid the accident, but swing the vehicle to the left or rightmight have a chance, which would violate the traffic rules by runninginto a wrong lane and have a chance to damage the self-driving motorvehicle, which would be your choice:

A. Brake the vehicle

B. Swing the vehicle.

EXAMPLE 2

At what risk degree between 0 and 1 would you take to damage yourvehicle or harm yourself to avoid running over pedestrians (0 indicatesnone, 1 indicates full)?

A. 0

B. 1

C. 0.5

D. Undecided.

EXAMPLE 3

When a collision between the self-driving motor vehicle and anothervehicle is unavoidable, which of the following would you choose?

A. Minimize the damage to yourself no matter what happens to the otherparty

B. Minimize the damage to yourself no matter what happens to the otherparty if the other party has the liability C. Take some risk of damagingyourself depending the circumstances to reduce the damage to the otherparty.

EXAMPLE 4

When an accident is unavoidable, which of the following would youchoose?

A. Minimize the damage to the passenger sitting on the front-left seatB. Minimize the damage to the passenger sitting on the back-right seat

C. Minimize the damage to myself no matter where I am sitting.

EXAMPLE 5

Your preferred driving style in highway is:

A. Smooth and steady

B. Swift and jerky

C. Aggressive with sport flavor.

An illustration to how to apply the two sets of user data in a real-timeoperation is given in FIG. 5. A robot or a service provider or a user ofa self-driving motor vehicle identifies a current user to be the currentuser, by identifying a current rider or a passenger, or one of currentriders or passengers of a self-driving motor vehicle being a user havingacquired data in the entry of the user in a scenario-user-choice pairdata set and/or having acquired data in the entry of the user in ascenario-user-choice pair data set and the entry of the user in a userprofile data set prior to a self-driving motor vehicle being practicallyused on a public roadway. When multiple users are riding a self-drivingmotor vehicle, it is mandatory to select one of the riders as thecurrent user and apply scenario-user-choice pair data of the currentuser in the scenario-user-choice pair data set or refer to user profiledata of the current user in the user profile data set in assisting theoperation of the vehicle. In case there is no passenger riding thevehicle, a default or selective set of factory settings, orscenario-user-choice pair data and/or user profile data of a designateduser could be elected in assisting the vehicle operation.

A self-driving motor vehicle should follow the rules and laws regardingvehicle operations in the first place. In response to a scenario duringa driving, the robot tries firstly to find a match between a currentscenario and a scenario in a scenario-user-choice pair in the entry ofthe current user in the scenario-user-choice pair data set, and operatesthe self-driving motor vehicle according to the operation behavior ofuser choice in the scenario-user-choice pair if the match is found,wherein if the current user is a current rider, the current user assumesat least partial responsibilities for consequences of the operating. Therobot proceeds to generate one or more optional operation behaviors ofthe self-driving motor vehicle if the match is not found, and in case ofmore than one optional operation behaviors being generated, estimateprobability for the current user to choose each of the operationbehaviors referencing data in the entry of the current user in the userprofile data set and data from statistical analysis of data of motorvehicle driving records and/or psychological behavior of drivers, andelecting an operation behavior having the largest probability to operatethe self-driving motor vehicle.

Sensing and matching a current scenario with an internal scenariorepresentation in a self-driving motor vehicle are both rudimentary andessential for operating a self-driving motor vehicle. Given that manytesting self-driving motor vehicles have been on the road for a fewyears, it is safe and sound to assume scenario matching has been anenabling procedure in the prior art of a self-driving motor vehicledesign, though room for improvements exists. Since the intention of thisinvention is to apply scenario matching in customizing a self-drivingmotor vehicle, this disclosure does not intend to elaborate on itsimplementations, though some illustrative examples are introduced belowbased on the illustrated design of a training collection:

EXAMPLE I:

Generate a vector Ci of a current scenario using the same protocol to ascenario in the training collection design embodiment previouslypresented in this specification,

Ci=(Ci[1], . . . Ci[n]), wherein

Ci[k] is coordinate of the K^(th) component of Ci, (k=1, 2, . . . , n),and 0<Ci[k]≤1;

Let Cj be a vector of n dimension representing a scenario in thescenario-user-choice pair data set,

Cj=(Cj[1], . . . Cj[n]), wherein

Cj[k] is coordinate of the K^(th) component Cj, (k=1, 2, . . . , n), and0<Cj[k]≤1.

Calculate similarity Sij:

Sij=(Σ(Ci[k]-Cj[k])∧2×α_(k)/(n×Σα_(k))∧0.5, (k=1, 2, . . . n) whereinα_(k) is a weighting factor for the k^(th) coordinate a Cj , 0<Cj[k]≤1;0<α_(k)≤1: Ci[k] and Cj[k] are matching coordinate components of Ci andCj.

if Sij is smaller than an adjustable threshold Tj, and Ci[k] is withinthe coordinate range of a Matching Boundary Vector Cjb which isexpressed as,

Cjb=[(Cjmin[1], Cjmax[1]), . . . (Cjmin[n], Cjmax[n])], (k=1, 2. . . ,n)

wherein 0<Cjmin[k]≤1;0<Cjmax[k]≤1; Cjmin[k] is the minimum coordinatevalue of the k^(th) component of Cjb, Cjmax[k] is the maximum coordinatevalue of the k^(th) component of Cjb;

Cjmin[k]≤Cj[k]≤Cjmax[k], and if an operation behavior Pj in scenario Cjis as effective in scenario Ci or the probability for Pj to be aseffective in scenario Ci is larger than a threshold Wj, scenario Ci isrecognized to be a matching scenario for scenario Cj.

EXAMPLE II

An alternative example embodiment to find a match of a current scenarioto a scenario in the scenario-user-choice pair data set is by searchingthrough Coordinate Matching Range Vector Set of all the scenarios in thescenario-user-choice pair data set,

Generate a vector Ci of a current scenario using the same protocol to ascenario in the training collection design embodiment previouslypresented in this specification,

Ci=(Ci[1], . . . Ci[n]), wherein

Ci[k] is coordinate of K^(th) component of Ci, (k=1, 2, . . . n), and0<Ci[k]≤1;

Search through all Coordinate Matching Range Vector Sets of all thescenarios in the scenario-user-choice pair data set, if coordinate ofeach component of vector Ci is found in a coordinate range of thecorresponding component of a Coordinate Matching Range Vector ofscenario Cj, scenario Cj is recognized to be a matching scenario forscenario Ci.

A simple example of applying scenario matching in a driving is asfollows:

for a scenario Ci:

a self-driving motor vehicle is driving in a local roadway at a speed of25 m/h; a pedestrian is crossing a local roadway 10 meter ahead;

operation behavior of user choice Pi in the scenario Ci is:

break to stop to avoid an accident;

assuming other factors are the same, a matching range of Ci for drivingspeed is 28-33 m/hour;

if in a current scenario Cj, driving speed is detected to be 32 m/hour,the current scenario Cj is therefore determined to match scenario Ci andoperation behavior of user choice brake to stop to avoid an accident Piis executed.

One example of categorizing the traits of a user into one of thefollowing groups in response to events in the Emergency Zone isillustrated in Example 4 as follows:

A. Habitual traffic violation offenders

B. Strict traffic rule followers

C. Smart and flexible drivers

D. Altruism volunteer heroes

It should be noted generating based on each category of user traits apreferred operation behavior of the vehicle is only of a probabilitynature. For example, in the Example 1 of the previously listed fiveexample scenarios, although there is a chance with a large probabilityfor traits B group users to select the answer A “brake”, while traits Cgroup users might select the answer B “swing”, it should not be assumedto be an affirmative action, of which the current user is not committedto take any responsibility for the consequence of the operation. Forevents in the Cruise Zone, a categorization based on user driving stylesin Example 5 is as follows:

A. Smooth and steady

B. Swift and jerky

C. Aggressive with sport flavor,

which could be rendered in the vehicle operations in favor of a userchoice or style when it is safe and lawful. Certain restrictions areapplied as a default setting for self-driving motor vehicles in general.For example, since this disclosure is not concerned about theapplication to use the driver-less technology for a battle vehicle in awar or for a vehicle for law enforcement, the self-driving motor vehicleis to be inhibited to be engaged in any offensive action against anythird parties, including pedestrians, other vehicles etc. It should alsobe barred from any self-destruction behavior comprising running out of acliff or against a road barrier or walls of a building, unless theControl System of the robot determines such a move is necessary forreducing the seriousness of an otherwise unavoidable accident and thecurrent user has optioned a choice of such an operation behavior in thescenario-user-choice pair data set. Although in general, applying userdata in a scenario-user-choice pair data set or a user profile data setis in operating a self-driving motor vehicle is intended to satisfyexperience and expectation of a user, there are exceptions on thecontrary, for example, if a user riding a self-driving motor vehicle isfound to be drunk by an alcoholic sensor, or to be a habitual recklessdriving offender, certain functions including user overriding the robotfor manually operating the vehicle should be restricted.

A continuing user customization by user adaptation and learning duringdriving is illustrated in FIG. 3 by module 380, particularly if thecurrent user is a recurrent passenger or an owner of the vehicle. In afirst example embodiment, a robot notifies the current user by promptingmessages and/or making announcements, through visual, sound or othertypes of media about an unfamiliar and/or untrained and/or hazardousroadway and traffic condition and asks for a guidance or command fromthe current user, executing the guidance or command in operation uponreceiving the guidance or command. Then the robot conducts evaluation ofthe effect or performance of the operation, and if the performance issatisfactory without an accident, generates data in the descriptive datapart of the scenario from the driving records, and takes the guidance orcommand as a user choice of operation behavior to form ascenario-user-choice pair, and with an explicit consent from the user,inserts the descriptive data into the entry of the user in thescenario-user-choice pair data set. An explicit consent from the user isrequired to make sure that the user knows a new data item of ascenario-user-choice pair being added and commits to assume at leastpartial responsibility for the consequence for the operation behavior ina matched scenario as a result of inserting the new data item. Theupdated scenario-user-choice pair data set is checked and used toextract and update data in the traits section of the user profile data,and the updated data sets will be applied in the driving afterwards.

In another example embodiment, the current user could take over thedriving physically when necessary if it is feasible in the design, and asimilar process to that used in the first example embodiment could beused based on a recorded scenario and driving behavior by the currentuser, to update the two user data sets by the same procedures as orsimilar procedures to procedures in the first example, comprising thesteps of:

recording and analyzing data of scenarios and driving behaviors of thecurrent user physically taking over operation of a self-driving motorvehicle; conducting an evaluation of performance of the user operationand generating data of scenario-user-choice pairs based on data of thescenarios and data of the driving behaviors of the current user;inserting data of the scenario-user-choice pairs into the entry of thecurrent user in the scenario-user-choice pair data set with explicitconsent from the current user if the performances being satisfactoryresulting in no accidents.

In another example embodiment, a robot could chat with a user through ahuman-machine interface and/or monitor a gaze, and/or a gesture of thecurrent user, and/or use other procedures to detect and analyze theuser's verbal, and/or tactile, and/or body languages reflecting his orher experiences and/or sentiments during the driving, and tune itsoperation accordingly, and the process could be used to expand and/orupdate the user profile data sets whenever applicable. Thereby, thecustomized operation of a self-driving motor could be incrementallyrefined.

The methods disclosed hereby could help resolve some of thecontroversial legal and/or moral issues including but not limited tothose illustrated in the examples above. For instance, the manufacturerand/or the service provider and/or the insurance provider of aself-driving motor vehicle operating on default factory settings isusually supposed to assume all liabilities for the vehicle being used.However, acquiring and applying scenario-user-choice pair data in ascenario-user-choice pair data set establishes a commitment between thevehicle and a user, wherein if the vehicle faithfully executes a userchoice of an operation behavior in a scenario matching a scenario in ascenario-user-choice pair in the entry of the current user in ascenario-user-choice pair data set, the current user assumes at leastpartial responsibilities for the consequences of the operation, whichcould resolve some of the controversial legal issues in addition tohaving the benefits of reducing areas of uncertainties and complexitiesin a Control System design.

One major hurdle in legalization of self-driving motor vehicles has beenthe concern over their impersonal and/or unpredictable behaviors inhandling abrupt, and/or unpredicted and/or conflicting-interest events.In that regard, an industry standard of a customized driving protocolbased on the methods for customizing driving of self-driving motorvehicles described above could serve as a means of legalizingself-driving motor vehicles, if accepted by the industry and the publicas well after road tests and other necessary evaluation/verificationprocesses. As an evidence of its efficacy of a utility, a trainingcollection used for acquiring a scenario-user-choice pair data set couldserve as a first module and performance criterion for such a protocol,since it reassures and manifests a well-defined verifiable and/orverified lawful operation behaviors accommodating any users, and aninstance of a user customization by acquiring a scenario-user-choicedata set based on a training collection as detailed above will result innothing less than predictable lawful vehicle operations, as if aconventional motor vehicle is driven by a human driver with at least abetter-than-average driving record. A comparison of operation behaviorsbetween a customized and non-customized generic self-driving motorvehicle as illustrated in FIG. 7 serves as a support to the idea,wherein Module 711 denotes a range of uncertain and/or unpredictableoperation behaviors without user customization, within the restraint oftraffic rules and laws, while module 712 comprising 711, denoting arange of operation behaviors with user customization incorporating userattributes, within the restraint of broader laws comprising trafficrules and laws.

Based on core processes of the methods of customized driving describedabove, a customized driving protocol for a self-driving motor vehicle ispresented as follows:

obtaining a training collection of data of a plurality of scenarios anda collection of data of operation behaviors of a self-driving motorvehicle in each of the scenarios, wherein the training collection areverified and/or verifiable by simulation and/or road tests and theoperation behaviors of a self-driving motor vehicle in each of thescenarios are lawful;

acquiring a scenario-user-choice pair data set based on the trainingcollection and acquiring a user profile data set of one or more users ofthe self-driving motor vehicle in manufacturing the self-driving motorvehicle and/or prior to driving the self-driving motor vehicle on apublic roadway in a practical service;

identifying a current rider, or one of current riders of theself-driving motor vehicle to be the current user, wherein data havebeen acquired in the entry of the current user in thescenario-user-choice pair data set and in the entry of the current userin the user profile data set of the self-driving motor vehicle prior tothe self-driving motor vehicle being practically used on a publicroadway;

driving the self-driving motor vehicle based on data in thescenario-user-choice pair data set and/or the user profile data set,comprising:

finding a match between a current scenario and a scenario in ascenario-user-choice pair in the entry of the current user in thescenario-user-choice pair data set;

operating the self-driving motor vehicle according to the user choice inthe scenario-user-choice pair if the match is found, and the currentuser assumes at least partial responsibilities for consequences of theoperating, or

generating operation behaviors of the self-driving motor vehicle if thematch is not found and estimating probability for the current user tochoose each of the operation behaviors referencing data in the entry ofthe current user in the user profile data set and data from statisticalanalysis of data of motor vehicle driving records and/or psychologicalbehavior of drivers, and electing an operation behavior having thelargest probability to operate the self-driving motor vehicle.

A self-driving motor vehicle operating driving according to thecustomized driving protocol should be eligible to apply for a license orpermit to carry a passenger in a practical service, and a license orpermit could be issued to the self-driving motor vehicle if otherfunctionalities including electromechanical features and performance ofthe self-driving motor vehicle driving in scenarios other than in thetraining collection are also qualified.

Two procedures, namely Procedure I and Procedure II are introducedwherein a manufacturer of self-driving motor vehicles or an authorizedor designated organization and/or individual evaluates if a self-drivingmotor vehicle could meet a first and/or a second performance criterionfor operating according to the customized driving protocol and issuesperformance certificate to the self-driving motor vehicle meeting thefirst and/or the second performance criterion for applying for a licenseor permit to carry a passenger in a practical service, wherein each ofthe two Procedures comprises two similar tests and Procedure Icomprising:

a first test examining the training collection for acquiring thescenario-user-choice pair data set adopted by a self-driving motorvehicle, checking the scope of coverage of the plurality of scenariosand the scope of coverage of the collection of operation behaviors ineach of the scenarios, wherein the self-driving motor vehicle passes thetest if the scope of coverage of the plurality of scenarios meets afirst performance benchmark and the scope of the collection of operationbehaviors meets a second performance benchmark, wherein the firstperformance benchmark comprises:

the plurality of scenarios in the training collection comprise a sub-setof scenarios in a reference training collection comprising all availableClass II scenarios at the time of the test and the ratio of the numberof the scenarios in the training collection over the number of thescenarios in the reference training collection is bigger than a firstthreshold Pt1, wherein the reference training collection could beobtained in a process such as in the example design of a trainingcollection through the steps of C1-C7.

wherein the second performance benchmark comprises:

the collection of operation behaviors in a scenario comprise a sub-setof operation behaviors in the scenario of the reference trainingcollection and an average value or a weighted average value respectingall the scenarios in the training collection of a ratio of the number ofoperation behaviors in a scenario in the training collection over thenumber of the operation behaviors in the scenario in the referencetraining collection is bigger than a second threshold Pt2, whereinvalues of the weighting factors to a ratio comprising depending upon theappearance probability and risk degree of the scenario.

a second test is conducted to verify the self-driving motor vehicleoperating driving according to the customized driving protocol, and ifthe self-driving motor vehicle operates driving according to thecustomized driving protocol, it passes the second test.

the self-driving motor vehicle meets the first performance criterion ifthe self-driving motor vehicle passes the first test and the secondtest.

In Procedure II, however, a third performance criterion is adopted,wherein the first test examines the scenario-user-choice pair data sethaving acquired data in an entry of the current user in place of thetraining collection, while the second test is the same as in ProcedureI. The data of the current user in the user profile data could also bechecked to determine conditions if or how to provide the service to thecurrent user, wherein the first test comprising:

examining the scenario-user-choice pair data set having acquired data inan entry of the current user, wherein the self-driving motor vehiclepasses the first test if the scenarios in an entry of the current userin the scenario-user-choice pair data set comprise a sub-set ofscenarios in a reference training collection comprising all availableClass II scenarios at the time of the test and the ratio of the numberof the scenarios in the user entry of the scenario-user-choice pair dataset over the number of scenarios in the reference training collection isbigger than a third threshold Pt3, wherein the reference trainingcollection could be obtained in a process such as in the example designof a training collection through the steps of C1-C7.

The training collection used as a first performance criterion forlegalizing self-driving motor vehicles could be designed by amanufacturer of self-driving motor vehicles, and/or by an institutionand/or an individual other than a manufacturer of self-driving motorvehicles in accordance with the rules and laws of an area, and/or of acity and/or of a state and/or of a country as a standard performancecriterion.

Design of a training collection could be carried out by complicatedalgorithms or just by ordinary skilled professionals with adequateknowledge, experience and training using relatively simple statisticaltools to process the statistical conventional vehicle operation dataand/or data from simulation and/or road tests of self-driving motorvehicles correlating with moral and/or ethics traits of users andtraffic rules and laws. Variations in controls and maneuverability ofself-driving motor vehicles, and different rules and laws in differentareas and/or countries require distinctive designs for a trainingcollection as a first performance criterion for legalizing self-drivingmotor vehicles. However, the introduced performance criteria as part ofthe methods of customization of self-driving motor vehicles forlegalizing self-driving motor vehicles is effective in increasingreliability and transparency in vehicle operation behaviors and reducingthe worries and panics from the legislators and the public over theirperformance in high risk, uncertain and conflicting-interest scenarios.

Customizing a self-driving motor vehicle in manufacturing comprises anefficient design of a Control System, being capable of fast accessingand processing the data in a scenario-user-choice pair data set, and/ora user profile data set; running fast scenario matching; efficientlyoperating according to a user choice in a matched scenario and/orrunning probabilistic analysis of data in a user profile data setcorrelating operation behaviors generated by a Control System.

In addition to the scheme outlined above, a custom design based onacquiring the user data sets in manufacturing process will furtherreduce design complexity and time to service and increase reliabilityand productivity as well. The user attribute data comprising thescenario-user-choice pair data set and the user profile data setcontaining data of one or more users whom a customer design targets areacquired and imported to the vehicle being manufactured, and areintegrated with the Control System and other parts of the vehicle,wherein simulations or road tests are run if needed, and the ControlSystem and other parts of the vehicle are tuned to an optimal conditionand settings of the vehicle are initialized according to specificationof the custom design before delivering the vehicle to a customer.

Based on the methods and procedures of customizing and legalizingself-driving motor vehicles described above, hereby is introduced asystem of customizing the operation of and legalizing self-driving motorvehicles, comprising:

Module 1, wherein a self-driving motor vehicle operating drivingaccording to a customized driving protocol; and

Module 2, wherein a manufacturer of a self-driving motor vehicle or anauthorized and/or designated organization and/or individual conductstests evaluating the performance of the self-driving motor vehicle inModule 1 and issues a performance certificate to the self-driving motorvehicle for applying for a license or a permit to provide a service tocarry a passenger, if the self-driving motor vehicle meets theperformance criterion by passing the tests comprised in Procedure Iand/or Procedure II described above.

In all, the methods and the system disclosed hereby should find themimplementable by ordinary skilled professionals in the field, and theapplicant would like to claim the rights and benefits to the scope ofthe disclosed invention as follows.

What is claimed is:
 1. A method of customizing and legalizing aself-driving motor vehicle comprising the steps of: obtaining a trainingcollection of data of a plurality of scenarios and a collection of dataof operation behaviors of a self-driving motor vehicle in each of thescenarios; acquiring a scenario-user-choice pair data set based on thetraining collection and a user profile data set of one or more users inmanufacturing the self-driving motor vehicle and/or prior to driving theself-driving motor vehicle on a public roadway in a practical service;identifying a current user to be the current user; operating theself-driving motor vehicle based on data of the current user in thescenario-user-choice pair data set and/or referencing data in the userprofile data set, comprising: finding successfully a match between acurrent scenario and a scenario in a scenario-user-choice pair in theentry of the current user in the scenario-user-choice pair data set;executing operation of the self-driving motor vehicle according to theoperation behavior of user choice in the scenario-user-choice pair,wherein if the current user is a current rider, the current user assumesat least partial responsibilities for consequences of the operating; orgenerating operation behaviors of the self-driving motor vehicle andestimating probability for the current user to choose each of theoperation behaviors comprising referencing data in the entry of thecurrent user in the user profile data set and electing an operationbehavior having the largest probability to operate the self-drivingmotor vehicle; wherein the training collection are verified and/orverifiable by simulation and/or road tests, and the operation behaviorsin the training collection are lawful, thereby comprises along with thescenario-user-choice pair data set performance criteria for theself-driving motor vehicle to apply for a license and/or a permit toprovide services to carry one or more passengers.
 2. The method of claim1, wherein the plurality of scenarios in the training collectioncomprising: presence of a conflict of interests involving attributes ofa user comprising moral and/or ethics traits, and/or laws and trafficrules, and/or risks to safety of a user and/or of a self-driving motorvehicle and/or of third parties; and/or presence of an uncertainty ofoperating a self-driving motor vehicle to match the intention of a userin response to abrupt events.
 3. The method of claim 1, wherein the stepof obtaining a training collection comprising: designing a trainingcollection and/or receiving a designed training collection or acombination of designing and receiving.
 4. The method of claim 1,wherein the step of acquiring of a scenario-user-choice pair data setcomprises: identifying a user; informing the user of a user choice of anoperation behavior in a scenario comprising a binding commitment betweena self-driving motor vehicle and the user, wherein the self-drivingmotor vehicle operates according to the operation behavior of the userchoice in a matched scenario, the user assumes at least partialresponsibility for the consequence of the operation behavior; obtaininga consent from the user to the binding commitment; presenting to theuser data of one scenario at a time of the scenarios in a trainingcollection and data of each of operation behaviors of a self-drivingvehicle in the scenario in the training collection; informing the userof the at least partial responsibility for the consequence of the eachof the operation behaviors in the scenario; obtaining a choice from theuser of an operation behavior in the scenario to form ascenario-user-choice pair of the scenario and the operation behavior;storing data of the scenario-user-choice pair in an entry of the user ina scenario-user-choice pair data set; repeating the steps frompresenting to storing for every scenario in the training collection; orreceiving data of scenario-user-choice pairs into an entry of the userin a scenario-user-choice pair data set of a self-driving motor vehicle,and confirming and/or updating the scenario-user-choice pair data setprior to the self-driving motor vehicle being practically used by theuser in a service on a public roadway.
 5. The method of claim 1, whereinthe step of acquiring a user profile data set comprising: identifying auser; obtaining background data of the user from information provided bythe user and/or by researching public records, storing the obtainedbackground data in a background section in an entry of the user in auser profile data set; extracting data of the traits of the user basedon the data in the entry of the user in a scenario-user-choice pair dataset and/or the data in the background section in the entry of the userin a user profile data set and storing the extracted data of the traitsof the user in a traits section in an entry of the user in the userprofile data set; or receiving background data and/or the extracted dataof the traits of the user, and confirming and/or updating the userprofile data set prior to a self-driving motor vehicle being practicallyused by the user on a public roadway.
 6. The method of claim 1, whereinthe step of identifying a user to be the current user comprising:identifying a current rider, or one of current riders of theself-driving motor vehicle to be the current user, wherein data havebeen acquired in the entry of the current user in thescenario-user-choice pair data set and in the entry of the current userin the user profile data set of the self-driving motor vehicle prior tothe self-driving motor vehicle being practically used on a publicroadway, or if no rider riding the self-driving motor vehicle,identifying a default or designated user to be the current user, whereindata have been acquired in the entry of the current user in thescenario-user-choice pair data set and in the entry of the current userin the user profile data set of the self-driving motor vehicle prior tothe self-driving motor vehicle being practically used on a publicroadway.
 7. The method of claim 1, comprising the self-driving motorvehicle updating the scenario-user-choice pair data set and/or the userprofile data set in the step of: executing a guidance or command by thecurrent user communicated to a robot of the self-driving motor vehiclethrough a user interface in a scenario in operation of the self-drivingmotor vehicle; conducting an evaluation of performances of theoperation; generating data of the scenario from the driving records;deriving data from the guidance or command from the current user as dataof a user choice of an operation behavior and constructing data of ascenario-user-choice pair; inserting the data of a scenario-user-choicepair into the entry of the user in the scenario-user-choice pair dataset with an explicit consent from the current user; and/or recording andanalyzing data of scenarios and driving behaviors of the current userphysically taking over operation of a self-driving motor vehicle;conducting an evaluation of performance of the user operation andgenerating data of scenario-user-choice pairs based on data of thescenarios and data of the driving behaviors of the current user;inserting data of the scenario-user-choice pairs into the entry of thecurrent user in the scenario-user-choice pair data set with explicitconsent from the current user if the performances being satisfactoryresulting in no accidents; and/or extracting data of user traits andinserting the extracted data of user traits into and/or updating data ofuser traits in the entry of the current user in the user profile dataset; and/or automatically detecting the facial and/or body languages ofthe current user reflecting experience and/or sentiment of the currentuser; tuning operations of the self-driving motor vehicle; extractingdata of user traits and inserting the extracted data of user traits intoand/or updating data of user traits in the entry of the current user inthe user profile data set.
 8. The method of claim 1, wherein theself-driving motor vehicle comprising being capable of: fast accessingand processing the data of scenario-user-choice pair data set, and/or ofthe user profile data set; running fast scenario matching, andefficiently operating the self-driving motor vehicle according to a userchoice in a matching scenario or based on probabilistic analysis of datain the user profile data set correlating with operation behaviorsgenerated by the Control System.
 9. The method of claim 1, comprising acustom design in manufacturing the self-driving motor vehicle in thesteps of: acquiring a scenario-user-choice pair data set and a userprofile data set of one or more users targeted by a customer design;integrating the acquired scenario-user-choice pair data set and a userprofile data set with the self-driving motor vehicle; runningsimulations and/or road tests if needed; tuning the self-driving motorvehicle to an optimal condition, and initializing settings of theself-driving motor vehicle according to specification of the customdesign before delivering the self-driving motor vehicle to a customer.10. The method of claim 3, wherein the designing a training collectioncomprising an embodiment of: C1. picking up scenarios into a trainingcollection in the steps of: collecting data of a collection of scenariosfrom conventional motor vehicle data and/or data from simulations androad tests of self-driving motor vehicles into a candidate set of atraining collection; assigning a first weighting factor Wc11 to each ofthe scenarios in the set, wherein the value of Wc11 comprisingproportional to appearance probability of the scenario based on astatistical analysis of the data of the scenarios in the candidate set;assigning a second weighting factor Wc12 to each of the scenarios,wherein the value of Wc12 comprising proportional to level of risk ofsafety of and/or damage to properties of parties involved in thescenario in relation to the intention of different drivers operating amotor vehicle in the scenario based on a statistical analysis of thedata of the scenarios in the candidate set; assigning a third weightingfactor Wc13 to a scenario, wherein the value of Wc13 comprises beingproportional to the level of uncertainty in operating a motor in thescenario based on a statistical analysis of the data of the scenarios inthe candidate set; finding a combined weight Wc10 comprising by aweighting average of the three weighting factors; sorting the candidateset in a descending order of the combined weight Wc10; selecting data ofscenarios in the candidate set into the training collection referencingin a top-down order to the combined weight Wc10, until the combinedweight Wc10 being smaller than a first adjustable threshold value Wc1t;C2. finding a candidate data set of operation behaviors for eachscenario in the training collection from a collection of data ofoperation behaviors in the scenario of conventional motor vehicle and/orof simulations and road tests of self-driving motor vehicles, whereineach operation behavior comprises one basic vehicle operation or asynchronized sequence of basic vehicle operations comprising speedingup, speeding down, moving forward or backing up, making turns, braking,lights and sound controls; finding the probability of appearance of eachof the operation behaviors in the candidate set by a statisticalanalysis; removing from the candidate set of data of operation behaviorsunlawful operation behaviors; removing from the candidate set of data ofoperation behaviors with probability of appearance smaller than a secondadjustable threshold value Pc2; C3. establishing a psychologicalbehavior probability density distribution based on a statisticalanalysis of data of motor vehicle driving records, and/or data fromsimulation of motor vehicle driving relating the moral and/or ethicstraits of a congregation of drivers and/or passengers; forming aone-dimensional probability density distribution between extreme selfishat one side and altruism at the other side, or a multiple dimensionaluser psychological behavior probability density distribution, anddividing the entire distribution domain into a plurality of segmentswherein each segment corresponding to a congregation of a group of userswith similar psychological behavior pattern of moral and/or ethicstraits; removing user groups with a segment probability smaller than athird adjustable threshold value Pc3; C4. determining based on astatistical analysis of data of motor vehicle driving records, and/ordata from simulation of motor vehicle driving and data of driving styleand/or moral and/or ethics traits of drivers and/or passengers aprobability P32[i][j] for a resulting user group i in step C3 to selectan operation behavior j in the resulting candidate set of data ofoperation behaviors in step C2, wherein i=(1,2 . . . , L), j=(1, 2 . . ., M); L denotes the number of the total resulting user groups in step ofC3 and M denotes the number of total operation behaviors in a resultingcandidate set in step of C2; electing an operation behavior from theresulting candidate set in step of C2 into the training collection as anoperation behavior for a user to choose to form a scenario-user-choicepair if the probability P32[i][j] being larger than an adjustablethreshold value P32t, or a user group dependent adjustable thresholdvalue P32t[i]; removing from the resulting candidate set the operationbehavior elected above to avoid duplicating; C5. optimizing the designby achieving a balancing between coverage of the scopes of the data andefficiency in actual usage of a training collection by adjusting theinvolved threshold values; C6. calculating a Coordinate Matching RangeVector Set associating with data of each scenario and data of eachoperation behavior in the scenario in the training collection forscenario matching in a driving, wherein: an algorithm for scenariomatching comprising: representing by a vector Cij in real space thenumeric data part of a scenario in a training collection measured,categorized, encoded, quantized and structured using a protocol of datain the descriptive data part of the scenario, Cij=(Cij[1], . . .Cij[n]), wherein Cij[k] is the coordinate of the K^(th) component ofvector Cij and (k=1, 2 . . . n), 0<Cij [k]≤1; representing by a vectorof n dimension Ci in real space the numeric data part of a currentscenario measured, categorized, encoded, quantized and structured usingthe same protocol of data in the descriptive data part of the scenario,Ci=(Ci[1], . . . Ci[n]), wherein Ci[k] is coordinate of the K^(th)component of vector Ci and (k=1, 2 . . . n), 0<Ci[k]≤1; and hereafter inthis implementation, any coordinate segment between two coordinateboundary values of a component of a vector form a coordinate range ofthe component of the vector, wherein each coordinate represent acorresponding monotonic measurement of a factor of the data set of ascenario, such that if any two boundary values of a coordinate segmentsatisfy a matching condition, all the coordinates within the segmentsatisfy the matching condition; letting Sij representing a similaritymeasurement between the two vectors Ci and Cij, and Sij=(Σ(Ci[k]-Cij[k])∧2× _(k)/(n×Σα_(k))∧0.5, (k=1, 2, . . . n), wherein α_(k) isa weighting factor for coordinate of the K^(th) component,0<Cij[k]≤1:0<Ci[k]≤1:0<α_(k)≤1, if Sij is smaller than an adjustablethreshold value Tij: determining an adjustable Matching Boundary VectorCijb around Cij based on a statistical analysis of data of motor vehiclerecords and/or data from simulations and road tests of motor vehicles,and Cijb=[(Cijbmin[1], Cijbmax[1]), (Cijbmin[n], . . . (Cijbmax[n])],wherein 0<Cijbmin[k]=1, 0<Cijbmax[k]≤1, and Cijbmin[k] is the minimumcoordinate of the k^(th) component of Cijb, and Cijbmax[k] is themaximum coordinate of the k^(th) component of Cijb, (k=1, 2, . . . , n);Cijbmin[k]≤Cij [k]≤Cijbmax[k], (k=1, 2, . . . , n); and Ci[k] is alsowithin a range of a Matching Boundary Vector Cijb,Cijbmin[k]≤Ci[k]≤Cijbmax[k], (k=1, 2, . . . n); letting Pij representingan operation behavior in scenario Cij, if Pij is as effective inscenario Ci or the probability for Pij to be as effective in scenario Ciis larger than an adjustable threshold value Wij, asserting scenario Cito be a match for scenario Cij under the condition of Pij; narrowingdown and/or refining the search range comprising: using a rangegranularity adjusting factor m equally dividing the range of eachcoordinate of each component of Coordinates Matching Range Vector Cijbinto m congruent segments with a range threshold value Tijbs[k]calculated by: Tijbs[k]=(Cijbmax[k]-Cijbmin[k])/m; (k=1, 2, . . . , n);expanding the Coordinates Matching Range Vector Cijb into an array Cijbacomprising coordinate segments with smaller range values in eachcomponent, and ${Cijba} = \begin{matrix}{( {{{{Cijbmin}\lbrack 1\rbrack}\lbrack 1\rbrack},{{{Cijbmax}\lbrack 1\rbrack}\lbrack 1\rbrack}} ),} & {( {{{{Cijbmin}\lbrack 1\rbrack}\lbrack 2\rbrack},{{{Cijbmax}\lbrack 1\rbrack}\lbrack 2\rbrack}} ),} & {\ldots,} & ( {{{{Cijbmin}\lbrack 1\rbrack}\lbrack m\rbrack},{{{Cijbmax}\lbrack 1\rbrack}\lbrack m\rbrack}} ) \\\vdots & \vdots & {\ldots,} & \vdots \\( {{{{Cijbmin}\lbrack n\rbrack}\lbrack 1\rbrack},{{{Cijbmax}\lbrack n\rbrack}\lbrack 1\rbrack}} ) & ( {{{{Cijbmin}\lbrack n\rbrack}\lbrack 2\rbrack},{{{Cijbmax}\lbrack n\rbrack}\lbrack n\rbrack}} ) & {\ldots,} & ( {{{{Cijbmin}\lbrack n\rbrack}\lbrack m\rbrack},{{{Cijbmax}\lbrack n\rbrack}\lbrack m\rbrack}} )\end{matrix}$ wherein, Cijbmin[k][1]is the minimum value of the L^(th)segment of the K^(th) component of the Coordinates Matching Range VectorCijb, and Cijbmax[k][1]is the maximum value of the L^(th) segment of theK^(th) component of the Coordinates Matching Range Vector Cijb, andCijbmax[k][1]=Cijbmin [k][1+1], (k=1, 2, . . . n); (1=1, 2, . . . ,m-1), Cijbmax [k][1] is equal to Cijbmin[k]; and Cijbmax [k][m] is equalto Cijbmax[k], respectively; constructing a candidate matching vector ofa scenario C_(ji) comprised of n coordinates with each of thecoordinates from a coordinate of a boundary point in a row of Cijba;finding all the vector C_(ji) matching Cij based on the algorithm forscenario matching and putting the matching vectors into a candidate set;constructing a Coordinate Matching Range Vector Cijr, wherein eachcoordinate is composed of a pair of coordinates comprising a firstcoordinate from a coordinate of a component of a vector in the candidateset and a second coordinate from a coordinate of the same component ofvector Cij, wherein the range of the pair of coordinates comprising amatching range of the coordinate values of the component; fine-tuningthe range granularity adjusting factor m to achieve a compromise betweenthe accuracy of a matching boundary and the data size of CoordinateMatching Range Vectors; C7. organizing all Coordinate Matching RangeVectors into a structured Coordinate Matching Range Vector Set Cijraassociating with data of each of the scenarios and data of each of theoperation behaviors in the each of the scenarios in the trainingcollection.
 11. The method of claim 1, wherein finding a match between acurrent scenario and a scenario in a scenario-user-choice pair in theentry of the current user in the scenario-user-choice pair data setcomprises a first embodiment and/or a second embodiment, wherein thefirst embodiment comprising the steps of: determining successfully thedeviation of each measurement of data of the current scenario beingwithin a boundary around a corresponding measurement of data of ascenario in a scenario-user-choice pair in the entry of the current userin the scenario-user-choice pair data set, wherein the boundary isestimated based on motor vehicle driving statistics data and/orsimulation data, and determining successfully a similarity measurementbetween a current scenario and the scenario in the scenario-user-choicepair in the entry of the current user in the scenario-user-choice pairdata set being smaller than a first threshold value, and determiningsuccessfully the operation behavior of a user choice in the scenario ofthe scenario-user-choice pair being as effective as in the currentscenario, or the probability for the operation behavior of a user choicein the scenario of the scenario-user-choice pair to be as effective asin the current scenario being larger than a second threshold value,wherein the second embodiment comprising the steps of: getting data of acurrent scenario measured, categorized, encoded, quantized andstructured into a vector using the same protocol as getting data of ascenario in the training collection measured, categorized, encoded,quantized and structured into a vector, and finding successfully thecoordinate range of each component of a Coordinate Matching Range Vectorin a Coordinate Matching Range Vector Set comprising the coordinate ofthe corresponding component of the vector representing the currentscenario.
 12. A system of customizing and legalizing a self-drivingmotor vehicle comprising: Module 1, wherein the self-driving motorvehicle operates according to a customized driving protocol; and Module2, wherein the self-driving motor vehicle in Module 1 is tested andissued a performance certificate for applying for a license or a permitto provide services to carry one or more passengers, if the self-drivingmotor vehicle meets qualifying performance criteria.
 13. The system ofclaim 12, wherein the customized driving protocol in Module 1comprising: obtaining a training collection of data of a plurality ofscenarios and a collection of data of operation behaviors of aself-driving motor vehicle in each of the scenarios, wherein thetraining collection are verified and/or verifiable by simulation and/orroad tests and the operation behaviors of a self-driving motor vehiclein each of the scenarios are lawful; acquiring a scenario-user-choicepair data set based on the training collection and acquiring a userprofile data set of one or more users of the self-driving motor vehiclein manufacturing the self-driving motor vehicle and/or prior to drivingthe self-driving motor vehicle on a public roadway in a practicalservice; identifying a current user to be the current user, comprising:identifying a current rider, or one of current riders of theself-driving motor vehicle to be the current user, wherein data havebeen acquired in the entry of the current user in thescenario-user-choice pair data set and in the entry of the current userin the user profile data set of the self-driving motor vehicle prior tothe self-driving motor vehicle being practically used on a publicroadway; operating the self-driving motor vehicle based on data of thecurrent user in the scenario-user-choice pair data set and/orreferencing data in the user profile data set, comprising: findingsuccessfully a match between a current scenario and a scenario in ascenario-user-choice pair in the entry of the current user in thescenario-user-choice pair data set; executing operation of theself-driving motor vehicle according to the operation behavior of userchoice in the scenario-user-choice pair, wherein if the current user isa current rider, the current user assumes at least partialresponsibilities for consequences of the operating; or generatingoperation behaviors of the self-driving motor vehicle; estimatingprobability for the current user to choose each of the operationbehaviors comprising referencing data in the entry of the current userin the user profile data set; electing an operation behavior having thelargest probability to operate the self-driving motor vehicle.
 14. Thesystem of claim 13, wherein acquiring a scenario-user-choice pair dataset of the customized driving protocol in Module 1 comprising:identifying a user; informing the user of a user choice of an operationbehavior in a scenario comprising a binding commitment between aself-driving motor vehicle and the user, wherein the self-driving motorvehicle operates according to the operation behavior of the user choicein a matched scenario, the user assumes at least partial responsibilityfor the consequence of the operation behavior; obtaining a consent fromthe user to the binding commitment; presenting to the user data of onescenario at a time of the scenarios in a training collection and data ofeach of operation behaviors of a self-driving vehicle in the scenario inthe training collection; informing the user of the at least partialresponsibility for the consequence of the each of the operationbehaviors in the scenario; obtaining a choice from the user of anoperation behavior in the scenario to form a scenario-user-choice pairof the scenario and the operation behavior; storing data of thescenario-user-choice pair in an entry of the user in ascenario-user-choice pair data set; repeating the steps from presentingto storing for every scenario in the training collection; or receivingdata of scenario-user-choice pairs into an entry of the user in ascenario-user-choice pair data set of a self-driving motor vehicle, andconfirming and/or updating the scenario-user-choice pair data set priorto the self-driving motor vehicle being practically used by the user ina service on a public roadway.
 15. The system of claim 13, whereinacquiring a user profile data set of the customized driving protocol inModule 1 comprising: identifying a user; obtaining background data ofthe user from information provided by the user and/or by researchingpublic records, storing the obtained background data in a backgroundsection in an entry of the user in a user profile data set; extractingdata of the traits of the user based on the data in the entry of theuser in a scenario-user-choice pair data set and/or the data in thebackground section in the entry of the user in a user profile data setand storing the extracted data of the traits of the user in a traitssection in an entry of the user in the user profile data set; orreceiving background data and/or the extracted data of the traits of theuser, and confirming and/or updating the user profile data set prior toa self-driving motor vehicle being practically used on a public roadwayby the user.
 16. The system of claim 13, wherein obtaining a trainingcollection of the customized driving protocol in Module 1 comprising:designing a training collection and/or receiving a designed trainingcollection or a combination of designing and receiving.
 17. The systemof claim 12, wherein the qualifying performance criteria in Module 2comprising a first qualifying performance criterion and/or a secondqualifying performance criterion, wherein the first qualifyingperformance criterion comprising: a first test of verifying successfullythe scenarios in the training collection comprise a sub-set of scenariosin a reference training collection, wherein the scenarios in thereference training collection comprising all available Class IIscenarios at the time of the test and the ratio of the number of thescenarios in the training collection over the number of the scenarios inthe reference training collection is bigger than a first threshold valuePt1, and the collection of operation behaviors in a scenario comprise asub-set of operation behaviors in the scenario of the reference trainingcollection and an average value or a weighted average value respectingall the scenarios in the training collection of a ratio of the number ofoperation behaviors in a scenario in the training collection over thenumber of the operation behaviors in the scenario in the referencetraining collection is bigger than a second threshold value Pt2, whereinvalues of the weighting factors to a ratio comprising depending upon theappearance probability and risk degree of the scenario; and a secondtest of verifying successfully the self-driving motor vehicle operatingcomplying with the customized driving protocol of Module
 1. 18. Thesystem of claim 17, wherein the second qualifying performance criterionin Module 2 comprising: a first test of verifying successfully thescenarios in an entry of the current user in the scenario-user-choicepair data set comprise a sub-set of scenarios in a reference trainingcollection, wherein the scenarios in the reference training collectioncomprising all available Class II scenarios at the time of the test andthe ratio of the number of the scenarios in the user entry of thescenario-user-choice pair data set over the number of scenarios in thereference training collection is bigger than a third threshold valuePt3, and a second test of verifying successfully the self-driving motorvehicle operating complying with the customized driving protocol ofModule
 1. 19. A process of design a training collection of data of aplurality of scenarios and a collection of data of operation behaviorsof a self-driving motor vehicle in each of the scenarios for customizingand legalizing self-driving motor vehicles comprising the steps of: V1.picking up data of a collection of Class II scenarios into a trainingcollection; V2. finding a candidate operation behavior set of data of acollection of lawful operation behaviors in each scenario in thetraining collection from a collection of data of lawful operationbehaviors in the scenario of conventional motor vehicle and/or ofsimulations and road tests of self-driving motor vehicles, wherein eachoperation behavior having a probability of appearance larger than afirst adjustable threshold value Pc2; V3. electing data of operationbehavior of each candidate operation behavior set associating with ascenario in the training collection as an operation behavior for a userto choose to form a scenario-user-choice pair in the scenario, if aprobability for the operation behavior to be selected in the scenario asa user choice by a congregation of users is larger than a secondthreshold value; V4. optimizing the design by achieving a balancingbetween coverage of the scopes of the data and efficiency in actualusage of a training collection by adjusting the involved thresholdvalues.
 20. The process of claim 19, wherein the step of V3 comprising:establishing a psychological behavior probability density distributionbased on a statistical analysis of data of motor vehicle drivingrecords, and/or data from simulation of motor vehicle driving relatingthe moral and/or ethics traits of a congregation of drivers and/orpassengers; forming a one-dimensional probability density distributionbetween extreme selfish at one side and altruism at the other side, or amultiple dimensional user psychological behavior probability densitydistribution, and dividing the entire distribution domain into aplurality of segments wherein each segment corresponding to acongregation of a group of users with similar psychological behaviorpattern of moral and/or ethics traits; removing user groups with asegment probability smaller than an adjustable threshold value Pc3;estimating probabilities for each of the remaining groups of users inthe step of V3 to select data of each operation behavior of each of thecandidate operation behavior sets of associating with data of eachscenario in a training collection based on statistical analysis ofconventional motor vehicle data and/or data from simulations and roadtests of self-driving motor vehicles including information about thedriving style and/or moral and/or ethics traits of drivers and/orpassengers; electing data of operation behaviors of each candidateoperation behavior set associating with a scenario in the trainingcollection as operation behaviors for a user to choose to form ascenario-user-choice pair in the scenario, if the probability for anyone of the remaining user groups in the step of V3 to select the data ofoperation behaviors of the candidate operation behavior set associatingwith the scenario in the training collection is larger than the secondthreshold value.